class_docstr: Random search oracle.
  
  Args:
      objective: A string, `keras_tuner.Objective` instance, or a list of
          `keras_tuner.Objective`s and strings. If a string, the direction of
          the optimization (min or max) will be inferred. If a list of
          `keras_tuner.Objective`, we will minimize the sum of all the
          objectives to minimize subtracting the sum of all the objectives to
          maximize. The `objective` argument is optional when
          `Tuner.run_trial()` or `HyperModel.fit()` returns a single float as
          the objective to minimize.
      max_trials: Integer, the total number of trials (model configurations)
          to test at most. Note that the oracle may interrupt the search
          before `max_trial` models have been tested if the search space has
          been exhausted. Defaults to 10.
      seed: Optional integer, the random seed.
      hyperparameters: Optional `HyperParameters` instance. Can be used to
          override (or register in advance) hyperparameters in the search
          space.
      tune_new_entries: Boolean, whether hyperparameter entries that are
          requested by the hypermodel but that were not specified in
          `hyperparameters` should be added to the search space, or not. If
          not, then the default value for these parameters will be used.
          Defaults to True.
      allow_new_entries: Boolean, whether the hypermodel is allowed to
          request hyperparameter entries not listed in `hyperparameters`.
          Defaults to True.
      max_retries_per_trial: Integer. Defaults to 0. The maximum number of
          times to retry a `Trial` if the trial crashed or the results are
          invalid.
      max_consecutive_failed_trials: Integer. Defaults to 3. The maximum
          number of consecutive failed `Trial`s. When this number is reached,
          the search will be stopped. A `Trial` is marked as failed when none
          of the retries succeeded.
code: def __init__(
      self,
      objective=None,
      max_trials=10,
      seed=None,
      hyperparameters=None,
      allow_new_entries=True,
      tune_new_entries=True,
      max_retries_per_trial=0,
      max_consecutive_failed_trials=3,
  ):
      super().__init__(
          objective=objective,
          max_trials=max_trials,
          hyperparameters=hyperparameters,
          tune_new_entries=tune_new_entries,
          allow_new_entries=allow_new_entries,
          seed=seed,
          max_retries_per_trial=max_retries_per_trial,
          max_consecutive_failed_trials=max_consecutive_failed_trials,
      )
  
file_path: keras-tuner/keras_tuner/tuners/randomsearch.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.engine import oracle
  from keras_tuner.engine import trial
  from keras_tuner.engine import tuner
label_desc: Use image node_id 1 to create a new RandomSearchOracle object from inherited base classes: oracle_module with example: obj = RandomSearchOracle(objective, max_trials, seed, hyperparameters, allow_new_entries, tune_new_entries, max_retries_per_trial, max_consecutive_failed_trials)